interpolate Method¶

The interpolate() method fills missing values using interpolation techniques, estimating values based on surrounding data points.

Linear Interpolation¶

The default method performs linear interpolation between valid values.

1. Basic Usage¶

import pandas as pd

url = "https://raw.githubusercontent.com/codebasics/py/master/pandas/5_handling_missing_data_fillna_dropna_interpolate/weather_data.csv"
df = pd.read_csv(url, index_col='day', parse_dates=True)
print(df)

dg = df.interpolate()
print(dg)

2. How Linear Works¶

For missing value at position i:

value[i] = value[i-1] + (value[i+1] - value[i-1]) / 2

3. Numeric Columns Only¶

Interpolation works on numeric columns; non-numeric columns are unchanged.

method Keyword¶

Specify the interpolation technique.

1. Linear (Default)¶

df.interpolate(method='linear')

Ignores the index and treats values as equally spaced.

2. Time-based¶

dg = df.interpolate(method='time')
print(dg)

Uses actual time intervals for interpolation. Requires DatetimeIndex.

3. Index-based¶

df.interpolate(method='index')

Uses the numerical values of the index.

Scipy Methods¶

Advanced interpolation methods via scipy.

1. Polynomial¶

df.interpolate(method='polynomial', order=2)

Fits a polynomial of specified order.

2. Spline¶

df.interpolate(method='spline', order=3)

Cubic spline interpolation for smooth curves.

3. Other Methods¶

# Available scipy methods:
# 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'
# 'krogh', 'pchip', 'akima', 'cubicspline'

Practical Comparison¶

Compare fillna with interpolate for time series data.

1. Forward Fill¶

df_ffill = df.fillna(method='ffill')
# Repeats last known value

2. Linear Interpolation¶

df_interp = df.interpolate()
# Estimates intermediate values

3. When to Use Each¶

# Use ffill for categorical or step-like data
# Use interpolate for continuous measurements

Time Series Example¶

Interpolate missing temperature readings.

1. Sample Data¶

dates = pd.date_range('2024-01-01', periods=5, freq='D')
temps = pd.Series([20, None, None, 26, 28], index=dates)
print(temps)

2. Linear Interpolation¶

temps.interpolate()
# Fills with 22 and 24 (evenly spaced)

3. Time Interpolation¶

temps.interpolate(method='time')
# Same result for equally spaced dates

Handling Edge Cases¶

Interpolation has limitations at boundaries.

1. Leading NaN¶

s = pd.Series([None, None, 3, 4, 5])
s.interpolate()  # Leading NaN remain

2. Trailing NaN¶

s = pd.Series([1, 2, 3, None, None])
s.interpolate()  # Trailing NaN remain

3. Combine with Fill¶

# First interpolate, then fill edges
s.interpolate().fillna(method='bfill').fillna(method='ffill')